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Neurobiological theories of spatial cognition developed with respect torecording data from relatively small and/or simplistic environments compared toanimals' natural habitats. It has been unclear how to extend theoretical modelsto large or complex spaces. Complementarily, in autonomous systems technology,applications have been growing for distributed control methods that scale tolarge numbers of low-footprint mobile platforms. Animals and many-robot groupsmust solve common problems of navigating complex and uncertain environments.Here, we introduce the 'NeuroSwarms' control framework to investigate whetheradaptive, autonomous swarm control of minimal artificial agents can be achievedby direct analogy to neural circuits of rodent spatial cognition. NeuroSwarmsanalogizes agents to neurons and swarming groups to recurrent networks. Weimplemented neuron-like agent interactions in which mutually visible agentsoperate as if they were reciprocally-connected place cells in an attractornetwork. We attributed a phase state to agents to enable patterns ofoscillatory synchronization similar to hippocampal models of theta-rhythmic(5-12 Hz) sequence generation. We demonstrate that multi-agent swarming andreward-approach dynamics can be expressed as a mobile form of Hebbian learningand that NeuroSwarms supports a single-entity paradigm that directly informstheoretical models of animal cognition. We present emergent behaviors includingphase-organized rings and trajectory sequences that interact with environmentalcues and geometry in large, fragmented mazes. Thus, NeuroSwarms is a modelartificial spatial system that integrates autonomous control and theoreticalneuroscience to potentially uncover common principles to advance both domains.

Cognitive swarming in complex environments with attractor dynamics and oscillatory computing